Ewerth, R., A. Balz, J. Gehlhaar, K. Dembczynski, and Eyke Hüllermeier. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” In In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany, edited by F. Hoffmann and Eyke Hüllermeier, 235–40. KIT Scientific Publishing, 2015.

Lu, S., and Eyke Hüllermeier. “Locally Weighted Regression through Data Imprecisiation.” In In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, 97–104. KIT Scientific Publishing, 2015.

Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification .” IEEE Transactions on Fuzzy Systems 23, no. 6 (2015): 2024–33.

Hüllermeier, Eyke. “From Knowledge-Based to Data Driven Fuzzy Modeling: Development, Criticism and Alternative Directions.” Informatik Spektrum 38, no. 6 (2015): 500–509.

Hüllermeier, Eyke. “Does Machine Learning Need Fuzzy Logic?” Fuzzy Sets and Systems 281 (2015): 292–99.

Waegeman, W., K. Dembczynski, A. Jachnik, W. Cheng, and Eyke Hüllermeier. “On the Bayes-Optimality of F-Measure Maximizers.” Journal of Machine Learning Research 15 (2015): 3313–68.

Shaker, A., and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.” Neurocomputing 150 (2015): 250–64.

Hüllermeier, Eyke, and M. Minor. “Case-Based Reasoning Research and Development .” In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer, 2015.

Hoffmann, F., and Eyke Hüllermeier. “Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing,” 2015.

Abdel-Aziz, A., and Eyke Hüllermeier. “Case Base Maintenance in Preference-Based CBR.” In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 1–14, 2015.

Szörényi, B., Robert Busa-Fekete, P. Weng, and Eyke Hüllermeier. “Qualitative Multi-Armed Bandits: A Quantile-Based Approach.” In In Proceedings International Conference on Machine Learning (ICML 2015), 1660–68, 2015.

Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using A Bilinear Plackett-Luce Model.” In In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 227–42, 2015.

Hüllermeier, Eyke, and W. Cheng. “Superset Learning Based on Generalized Loss Minimization .” In In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 260–75, 2015.

Henzgen, Sascha, and Eyke Hüllermeier. “Weighted Rank Correlationf: A Flexible Approach Based on Fuzzy Order Relations.” In In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 422–37, 2015.

Szörényi, B., Robert Busa-Fekete, Adil Paul, and Eyke Hüllermeier. “Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach.” In In Advances in Neural Information Processing Systems 28 (NIPS 2015), 604–12, 2015.

Szörényi, B., Robert Busa-Fekete, K. Dembczynski, and Eyke Hüllermeier. “Online F-Measure Optimization.” In In Advances in Neural Information Processing Systems 28 (NIPS 2015), 595–603, 2015.

El Mesaoudi-Paul, Adil, and Eyke Hüllermeier. “A CBR Approach to the Angry Birds Game.” In In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 68–77, 2015.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML), 110–11, 2015.

Lu, S., and Eyke Hüllermeier. “Locally Weighted Regression through Data Imprecisiation.” In Proceedings 25. Workshop Computational Intelligence, 97–104, 2015.

Ewerth, Ralph, A. Balz, J. Gehlhaar, K. Dembczynski, and Eyke Hüllermeier. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” In Proceedings 25. Workshop Computational Intelligence, 235–40, 2015.

Waegeman, W., K. Dembczynski, A. Jachnik, W. Cheng, and Eyke Hüllermeier. “On the Bayes-Optimality of F-Measure Maximizers.” In Journal of Machine Learning Research 15 (2015): 3333–88.

Hüllermeier, Eyke. “Does Machine Learning Need Fuzzy Logic?” Fuzzy Sets and Systems 281 (2015): 292–99.

Shaker, Ammar, and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.” Neurocomputing 150 (2015): 250–64.

Hüllermeier, Eyke. “From Knowledge-Based to Data-Driven Fuzzy Modeling-Development, Criticism and Alternative Directions.” Informatik Spektrum 38, no. 6 (2015): 500–509.

Garcia-Jimenez, S., U. Bustince, Eyke Hüllermeier, R. Mesiar, N.R. Pal, and A. Pradera. “Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems.” IEEE Transactions on Fuzzy Systems 23, no. 4 (2015): 1259–73.

Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning of Classification.” IEEE Transactions on Fuzzy Systems 23, no. 6 (2015): 2024–33.

Basavaraju, Manu, L Sunil Chandran, Deepak Rajendraprasad, and Arunselvan Ramaswamy. “Rainbow Connection Number of Graph Power and Graph Products.” Graphs and Combinatorics 30, no. 6 (2014): 1363–82.

Basavaraju, Manu, L Sunil Chandran, Deepak Rajendraprasad, and Arunselvan Ramaswamy. “Rainbow Connection Number and Radius.” Graphs and Combinatorics 30, no. 2 (2014): 275–85.

Mohr, Felix, and Sven Walther. “Template-Based Generation of Semantic Services.”https://doi.org/10.1007/978-3-319-14130-5_14__In Proceedings of the 14th International Conference on Software Reuse (ICSR), 188–203. LNCS, 2014.https://doi.org/10.1007/978-3-319-14130-5_14 .

Jungmann, Alexander, Felix Mohr, and Bernd Kleinjohann. “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services.”https://doi.org/10.1109/SERVICES.2014.68__In Proceedings of the 10th World Congress on Services (SERVICES), 346–53, 2014.https://doi.org/10.1109/SERVICES.2014.68 .

Jungmann, Alexander, Felix Mohr, and Berndhttps://doi.org/10.1109/SOCA.2014.48__Kleinjohann. “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition.”https://doi.org/10.1109/SOCA.2014.48__In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 105–12, 2014.https://doi.org/10.1109/SOCA.2014.48 .

Mohr, Felix. “Estimating Functional Reusability of Services.” In Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 411–18. LNCS, 2014.

Agarwal, M., A. Fallah Tehrani, and Eyke Hüllermeier. “Preference-Based Learning of Ideal Solutions in TOPSIS-like Decision Models.” Journal of Multi-Criteria Decision Analysis 22, no. 3–4 (2014).

Krotzky, T., T. Fober, Eyke Hüllermeier, and G. Klebe. “Extended Graph-Based Models for Enhanced Similarity Search in Cabase.” IEEE/ACM Transactions of Computational Biology and Bioinformatics 11, no. 5 (2014): 878–90.

Hüllermeier, Eyke. “Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” International Journal of Approximate Reasoning 55, no. 7 (2014): 1519–34.

Henzgen, Sascha, M. Strickert, and Eyke Hüllermeier. “Visualization of Evolving Fuzzy-Rule-Based Systems.” Evolving Systems 5 (2014): 175–91.

Busa-Fekete, Robert, B. Szörenyi, P. Weng, W. Cheng, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning: Evolutionary Direct Policy Search Using a Preference-Based Racing Algorithm.” Machine Learning 97, no. 3 (2014): 327–51.

Krempl, G., I. Zliobaite, D. Brzezinski, Eyke Hüllermeier, M. Last, V. Lemaire, T. Noack, et al. “Open Challenges for Data Stream Mining Research.” SIGKDD Explorations 16, no. 1 (2014): 1–10.

Strickert, M., K. Bunte, F.M. Schleif, and Eyke Hüllermeier. “Correlation-Based Embedding of Pairwise Score Data.” Neurocomputing 141 (2014): 97–109.

Shaker, Ammar, and Eyke Hüllermeier. “Survival Analysis on Data Streams: Analyzing Temporal Events in Dynamically Changing Environments.” International Journal of Applied Mathematics and Computer Science 24, no. 1 (2014): 199–212.

Senge, Robin, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, and Eyke Hüllermeier. “Reliable Classification: Learning Classifiers That Distinguish Aleatoric and Epistemic Uncertainty.” Information Sciences 255 (2014): 16–29.

Donner-Banzhoff, N., J. Haasenritter, Eyke Hüllermeier, A. Viniol, S. Bösner, and A. Becker. “The Comprehensive Diagnostic Study Is Suggested as a Design to Model the Diagnostic Process.” Journal of Clinical Epidemiology 2, no. 67 (2014): 124–32.

Busa-Fekete, Robert, B. Szörényi, and Eyke Hüllermeier. “PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences.” In Proceedings AAAI 2014, Quebec, Canada, 1701–7, 2014.

Busa-Fekete, Robert, and Eyke Hüllermeier. “A Survey of Preference-Based Online Learning with Bandit Algorithms.” In Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia, 18–39, 2014.

Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” In Proceedings Discovery Science, Bled,Slovenia , 123–34, 2014.

Fallah Tehrani, A., M. Strickert, and Eyke Hüllermeier. “The Choquet Kernel for Monotone Data.” In Proceedings ESANN , Bruges, Belgium, 2014.

Abdel-Aziz, A., M. Strickert, and Eyke Hüllermeier. “Learning Solution Similarity in Preference-Based CBR.” In Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland, 17–31, 2014.

Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using A Bilinear Plackett-Luce Model.” In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany, 32–33, 2014.

Calders, T., F. Esposito, Eyke Hüllermeier, and R. Meo. “Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France.” In Proceedings, Parts I-III. Lecture Notes in Computer Science, 8724–26. Springer, 2014.

Fürnkranz, J., Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, and Scott Sanner. “Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports,” 4:1–27, 2014.

Shaker, Ammar, and Eyke Hüllermeier. “Survival Analysis on Data Streams: Analyzing Temporal Events in Dynamically Changing Environments.” Applied Mathematics and Computer Science 24, no. 1 (2014): 199–212.

Hoffmann, F., Eyke Hüllermeier, and A. Kroll. “Ausgewählte Beiträge Des GMA-Fachausschusses 5.14.” Computational Intelligence Automatisierungstechnik 62, no. 10 (2014): 685–86.

Calders, T., F. Esposito, Eyke Hüllermeier, and R. Meo. “Guest Editors`introduction:Special Issue of the ECML/PKDD 2014 Journal Track.” Data Min. Knowledge Discovery 28, no. 5–6 (2014): 1129–33.

Henzgen, Sascha, M. Strickert, and Eyke Hüllermeier. “Visualization of Evolving Fuzzy Rule-Based Systems.” Evolving Systems 5, no. 3 (2014): 175–91.

Hüllermeier, Eyke. “Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” Int. J. Approx. Reasoning 55, no. 7 (2014): 1519–34.

Hüllermeier, Eyke. “Rejoinder on "Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” Int. J. Approx. Reasoning 55, no. 7 (2014): 1609–13.

Strickert, M., K. Bunte, F.- M. Schleif, and Eyke Hüllermeier. “Correlation-Based Embedding of Pairwise Score Data.” Neurocomputing 141 (2014): 97–109.

Senge, Robin, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, and Eyke Hüllermeier. “Reliable Classification: Learning Classifiers That Distinguish Aleatoric and Epistemic Uncertainty.” Information Sciences 255 (2014): 16–29.

Mernberger, M., M. Moog, S. Stork, S. Zauner, U.G. Maier, and Eyke Hüllermeier. “Protein Sub-Cellular Localization Prediction for Special Compartments via Optimized Time Series Distances.” J. Bioinformatics and Computational Biology 12, no. 1 (2014).

Calders, T., F. Esposito, Eyke Hüllermeier, and R. Meo. “Guest Editors`introduction:Special Issue of the ECML/PKDD 2014 Journal Track.” Machine Learning 97, no. 1–2 (2014): 1–3.

Busa-Fekete, Robert, B. Szörényi, P. Weng, W. Cheng, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning: Evolutionary Direct Policy Search Using a Preference-Based Racing Algorithm.” Machine Learning 97, no. 3 (2014): 327–51.

Montanés, E., Robin Senge, J. Barranquero, J.R. Quevedo, J.J. Del Coz, and Eyke Hüllermeier. “Dependent Binary Relevance Models for Multi-Label Classification.” Pattern Recognition 47, no. 3 (2014): 1494–1508.

Krempl, G., I. Zliobaite, D. Brzezinski, Eyke Hüllermeier, M. Last, V. Lemaire, T. Noack, et al. “Open Challenges for Data Stream Mining Research.” SIGKDD Explorations 16, no. 1 (2014): 1–10.

Krotzky, T., T. Fober, Eyke Hüllermeier, and G. Klebe. “Extended Graph-Based Models for Enhanced Similarity Search in Cavbase.” IEEE/ACM Trans. Comput. Biology Bioinform. 11, no. 5 (2014): 878–90.

Stock, M., T. Fober, Eyke Hüllermeier, S Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets, and W. Wageman. “Identification of Functionally Releated Enzymes by Learning to Rank Methods.” IEEE/ACM Trans. Comput. Biology Bioinform. 11, no. 6 (2014): 1157–69.

Mohr, Felix, and Hans Kleine Büning. “Semi-Automated Software Composition Through Generated Components.”https://doi.org/10.1145/2539150.2539235__In Proceedings of the 15th International Conference on Information Integration and Web-Based Applications &; Services (IiWAS), 676–80, 2013.https://doi.org/10.1145/2539150.2539235 .

Mohr, Felix, Theodor Lettmann, and Hans Kleine Büning. “Reducing Nondeterminism in Automated Service Composition.”https://doi.org/10.1109/SOCA.2013.25__In Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 154–61, 2013.https://doi.org/10.1109/SOCA.2013.25 .

Cheng, W., S. Henzgen, and Eyke Hüllermeier. “Labelwise versus Pairwise Decomposition in Label Ranking.” In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 129–36, 2013.

Senge, Robin, J. del Coz, and Eyke Hüllermeier. “Rectifying Classifier Chains for Multi-Label Classification, Bamberg, Germany.” In In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany, 151–58, 2013.

Busa-Fekete, Robert, T. Fober, and Eyke Hüllermeier. “Preference-Based Evolutionary Optimization Using Generalized Racing Algorithms.” In In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, 237–46. KIT Scientific Publishing, 2013.

Henzgen, S., and Eyke Hüllermeier. “Weighted Rank Correlation Measures Based on Fuzzy Order Relations.” In In Proceedings 23th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, 227–36. KIT Scientific Publishing, 2013.

Weng, P., Robert Busa-Fekete, and Eyke Hüllermeier. “Interactive Q-Learning with Ordinal Rewards and Unreliable Tutor.” In In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.

Busa-Fekete, Robert, B. Szörenyi, P. Weng, and Eyke Hüllermeier. “Preference-Based Evolutionary Direct Policy Search.” In In Proceedings ECML/PKDD-Workshop on Reinforcement Learning from Generalized Feedback:Beyond Numerical Rewards, Prague, 2013.

Cheng, W., and Eyke Hüllermeier. “A Nearest Neigbor Approach to Label Ranking Based on Generalized Labelwise Loss Minimization.” In In Proceedings M-PREF, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China, 2013.

Shaker, Ammar, and Eyke Hüllermeier. “Event History Analysis on Data Streams: An Application to Earthquake Occurence.” In In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic, edited by G. Krempl, I. Zliobaite, Y. Wang, and G. Forman, 38–41, 2013.

Senge, Robin, J.J. del Coz, and Eyke Hüllermeier. “On the Problem of Error Propagation in Classier Chains for Multi-Label Classification. Data Analysis, Machine Learning and Knowledge Discovery.” In In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany , edited by L. Schmidt-Thieme and M. Spiliopoulou. Springer, 2013.

Fober, T., G. Klebe, and Eyke Hüllermeier. “Local Clique Merging: An Extension of the Maximum Common Subgraph Measure with Applications in Structural Bioinformatics, Algorithms from and for Nature and Life.” In In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany, edited by B. Lausen, D. Van den Poel, and A. Ultsch, 279–86. Springer, 2013.

Fallah Tehrani, A., and Eyke Hüllermeier. “Ordinal Choquistic Regression .” In In Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy, edited by J. Montero, G. Pasi, and D. Ciucci. Atlantis Press, 2013.

Nasiri, N., T. Fober, Robin Senge, and Eyke Hüllermeier. “Fuzzy Pattern Trees as an Alternative to Rule-Based Fuzzy Systems: Knowledge-Driven, Data-Driven and Hybrid Modeling of Colour Yield in Poyester Dyeing, Edmonton, Canada.” In In Proceedings IFSA-2013 World Congress of the International Fuzzy Systems Association, Edmonton, Canada, 715–21, 2013.

Heider, D., Robin Senge, W. Cheng, and Eyke Hüllermeier. “Multilabel Classification for Exploiting Cross-Resistance Information in HIV-1 Drug Resistence Prediction.” Bioinformatics 29, no. 16 (2013): 1946–52.

Bösner, S., K. Bönisch, J. Haasenritter , P. Schlegel, Eyke Hüllermeier, and N. Donner-Banzhoff. “Chest Pain in Primary Care: Is the Localization of Pain Diagnostically Helpful in the Critical Evaluation of Patients? A Cross Sectional Study. .” BMC Family Practice 14, no. 1 (2013): 154–62.

Haasenritter, J., A. Viniol, A. Becker, S. Bösner, Eyke Hüllermeier, Robin Senge, and N. Donner-Banzhoff. “Diagnose Im Kontext - Eine Erweiterte Perspektive.” Zeitschrift Für Evidenz, Fortbildung Und Qualität Im Gesundheitswesen (ZEFQ) 107 (2013): 585–91.

Shaker, A., R. Senge, and Eyke Hüllermeier. “Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams.” Information Sciences 220 (2013): 34–45.

Szarvas, G., Robert Busa-Fekete, and Eyke Hüllermeier. “Learning to Rank Lexical Substitutions.” In In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013.

Dembczynski, K., A. Jachnik, W. Kotlowski, W. Waegeman, and Eyke Hüllermeier. “Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.” In In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, edited by S. Dasgupta and D. McAllester, 1130–38, 2013.

Busa-Fekete, Robert, B. Szoreny, P. Weng, W. Cheng, and Eyke Hüllermeier. “Top-k Selection Based on Adaptive Sampling of Noisy Preferences.” In In Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, edited by S. Dasgupta and D. McAllester, 1094–1102, 2013.

Hüllermeier, Eyke, and W. Cheng. “Preference-Based CBR: General Ideas and Basic Principles.” In In Proceedings IJCAI-13, 23rd International Joint Conference on Artificial Intelligence, Beijing, China, edited by F. Rossi, 3012–16. AAAI Press, 2013.

Henzgen, Sascha, M. Strickert, and Eyke Hüllermeier. “Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems.” In In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, edited by R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, and A. Zolnierek, 279–88. Springer, 2013.

Shaker, Ammar, and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams.” In In Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, edited by R. Burduk, K. Jackowski, M. Kurzynski, W. Wozniak, and A. Zolnierek, 289–98. Springer, 2013.

Leinweber, M., L. Baumgärtner, M. Mernberger, T. Fober, Eyke Hüllermeier, G. Klebe, and B. Freisleben. “GPU-Based Cloud Computing for Comparing the Structure of Protein Binding Sites.” In In Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione D`Italia, Italy, 2012.

Hüllermeier, Eyke, and A. Fallah Tehrani. “Efficient Learning of Classifiers Based on the 2-Additive Choquet Integral.” In Computational Intelligence in Intelligent Data Analysis, edited by C. Moewes and A. Nürnberger, 17–30. Studies in Computational Intelligence. Springer, 2012.

Bräuning, M., and Eyke Hüllermeier. “Learning Conditional Lexicographic Preference Trees.” In In Workshops on Preference Learning at ECAI, European Conference on Artiticial Intelligence, Montpellier, France, 2012.

Hüllermeier, Eyke, and A. Fallah Tehrani. “On the VC Dimension of the Choquet Integral.” In In Proceedings IPMU-2012 14th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy, 2012.

Fallah Tehrani, A., W. Cheng, K. Dembczynski, and Eyke Hüllermeier. “Learning  Monotone Nonlinear Models Using the Choquet Integral.” Machine Learning 89, no. 1 (2012): 183–211.

Bustince, H., M. Pagola, R. Mesiar, Eyke Hüllermeier, and F. Herrera. “Grouping, Overlap and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons.” IEEE Transactions on Fuzzy Systems 20, no. 3 (2012): 405–15.

Fürnkranz, J., Eyke Hüllermeier, W. Cheng, and S.H. Park. “Preference-Based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm.” Machine Learning 89, no. 1 (2012): 123–56.

Dembczynski, K., W. Waegeman, W. Cheng, and Eyke Hüllermeier. “On Label Dependence and Loss  Minimization in Multi-Label Classification.” Machine Learning 88, no. 1–2 (2012): 5–45.

Shaker, Ammar, and Eyke Hüllermeier. “IBL Streams: A System for Instance-Based Classification and Regression on Data Streams.” Evolving Systems 3, no. 4 (2012): 235–49.

Dolorez Ruiz, M., and Eyke Hüllermeier. “A Formal and Empirical Analysis of the Fuzzy Gamma Rank Correlation Coefficient.” Information Sciences 206 (2012): 1–17.

Fallah Tehrani, A., W. Cheng, and Eyke Hüllermeier. “Preference Learning Using the Choquet Integral: The Case of Multipartite Ranking.” IEEE Transactions on Fuzzy Systems 20, no. 6 (2012): 1102–13.